11 papers · 1 filter
A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification
Kehan Long, Yiqi Zhao, Pol Mestres +3
Uncertainty quantification from finite data is central to machine learning, optimization, and automation systems, where decisions must remain reliable under limited samples and tes…
Safe Feedback Optimization through Control Barrier Functions
Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés +1
Feedback optimization refers to a class of methods that steer a control system to a steady state that solves an optimization problem. Despite tremendous progress on the topic, an i…
Universal Formulas for Safe Control and Their Neural Network Approximations
Pol Mestres, Jorge Cortés, Eduardo D. Sontag
We study the problem of designing a controller that satisfies an arbitrary number of affine inequalities at every point in the state space. This is motivated by the fact that a var…
Feedback Optimization with State Constraints through Control Barrier Functions
Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés +1
Recently, there has been a surge of research on a class of methods called feedback optimization. These are methods to steer the state of a control system to an equilibrium that ari…
Control Barrier Function-Based Safety Filters: Characterization of Undesired Equilibria, Unbounded Trajectories, and Limit Cycles
Pol Mestres, Yiting Chen, Emiliano Dall'anese +1
This paper focuses on safety filters designed based on Control Barrier Functions (CBFs): these are modifications of a nominal stabilizing controller typically utilized in safety-cr…
Stabilization of Nonlinear Systems through Control Barrier Functions
Pol Mestres, Kehan Long, Melvin Leok +2
This paper proposes a control design approach for stabilizing nonlinear control systems. Our key observation is that the set of points where the decrease condition of a control Lya…